from __future__ import division
from functions import *
from utils import *
%matplotlib inline
%load_ext autoreload
%autoreload 2
%%javascript
IPython.OutputArea.auto_scroll_threshold = 9999;
mouseA = '/Volumes/DATA/DATA/Equalized Separation/2014 Oct 27/'
mouseB = '/Volumes/DATA/DATA/Equalized Separation/2014 Oct 22/'
# mouseA = '/Users/guillaume/Projects/GEVI-DATA/2014 Oct 27/'
# mouseB = '/Users/guillaume/Projects/GEVI-DATA/2014 Oct 22/'
dataA = Parallel(n_jobs=8)(delayed(getExpData)(i, mouseA) for i in [3,4,5,6])
dataB = Parallel(n_jobs=8)(delayed(getExpData)(i, mouseB) for i in [2,3,4,5])
discard = {
'MouseA':
{
3 : [1,2,3,4,5],
4 : [1,2],
6 : [5]
},
'MouseB' :
{
3 : [2,4,5],
4 : [1,2]
}
}
goodDataA = keepGoodData(dataA, discard['MouseA'],3)
goodDataB = keepGoodData(dataB, discard['MouseB'],2)
# data[exp][0:ratio 1:hemo][repeat][x][y]
np.array(goodDataA[2][0]).shape
for i in range(len(dataA)):
plotData(dataA[i], i, discard['MouseA'], start=3)
for i in range(len(dataB)):
plotData(dataB[i], i, discard['MouseB'],2)
fig = plt.figure(figsize=(11,5))
ax = fig.add_subplot(1,2,1)
alpha = getMeanAlphaFiltered(dataA,1,100)
ax.plot(alpha)
ax.set_title('Mean transfer function mouse A')
ax = fig.add_subplot(1,2,2)
alpha = getMeanAlphaFiltered(dataB,1,100)
ax.plot(alpha)
ax.set_title('Mean transfer function mouse B')
fig = plt.figure(figsize=(11,5))
ax = fig.add_subplot(1,2,1)
alpha = getMeanAlphaFiltered(goodDataA,1,100)
ax.plot(alpha)
ax.set_title('Mean transfer function mouse A')
ax = fig.add_subplot(1,2,2)
alpha = getMeanAlphaFiltered(goodDataB,1,100)
ax.plot(alpha)
ax.set_title('Mean transfer function mouse B')
# compareCorr(data[1],data[3])
fig = plt.figure(figsize=(10,10))
for i in range(len(dataA)):
ax = fig.add_subplot(2,2,i+1)
plotCorrRatioAlpha(ax, dataA[i], getMeanAlphaFiltered(goodDataA,0,300), discard['MouseA'], exp = i, start = 3)
plt.suptitle('Voltage Mouse A', fontsize=26)
fig = plt.figure(figsize=(10,10))
for i in range(len(dataB)):
ax = fig.add_subplot(2,2,i+1)
plotCorrRatioAlpha(ax, dataB[i], getMeanAlphaFiltered(goodDataB,0,300), discard['MouseB'], exp = i, start = 2)
plt.suptitle('Voltage Mouse B', fontsize=26)
fig = plt.figure(figsize=(10,10))
for i in range(len(dataA)):
ax = fig.add_subplot(2,2,i+1)
plotCorrHemoAlpha(ax, dataA[i], getMeanAlphaFiltered(goodDataA,0,100), discard['MouseA'], exp = i, start = 3)
plt.suptitle('Hemo Mouse A', fontsize=26)
fig = plt.figure(figsize=(10,10))
for i in range(len(dataB)):
ax = fig.add_subplot(2,2,i+1)
plotCorrHemoAlpha(ax, dataB[i], getMeanAlphaFiltered(goodDataB,0,100), discard['MouseB'], exp = i, start = 2)
plt.suptitle('Hemo Mouse B', fontsize=26)
## With the 'bad' data
# # compareCorr(data[1],data[3])
# fig = plt.figure(figsize=(10,10))
# for i in range(len(dataA)):
# ax = fig.add_subplot(2,2,i+1)
# plotCorrRatioAlpha(ax, dataA[i], getMeanAlphaFiltered(dataA,0,300), exp = i, start = 3)
fig = plt.figure(figsize=(10,10))
for i in range(len(dataA)):
ax = fig.add_subplot(2,2,i+1)
plotCorrRatioAlpha(ax, dataA[i], getMeanAlphaFiltered([dataA[i]],0,300), discard['MouseA'], exp = i, start = 3)
plt.suptitle('Voltage Mouse A', fontsize=26)
fig = plt.figure(figsize=(10,10))
for i in range(len(dataB)):
ax = fig.add_subplot(2,2,i+1)
plotCorrRatioAlpha(ax, dataB[i], getMeanAlphaFiltered([dataB[i]],0,300), discard['MouseB'], exp = i, start = 2)
plt.suptitle('Voltage Mouse B', fontsize=26)
fig = plt.figure(figsize=(10,10))
for i in range(len(dataA)):
ax = fig.add_subplot(2,2,i+1)
plotCorrHemoAlpha(ax, dataA[i], getMeanAlphaFiltered([dataA[i]],0,100), discard['MouseA'], exp = i, start = 3)
plt.suptitle('Hemo Mouse A', fontsize=26)
fig = plt.figure(figsize=(10,10))
for i in range(len(dataB)):
ax = fig.add_subplot(2,2,i+1)
plotCorrHemoAlpha(ax, dataB[i], getMeanAlphaFiltered([dataB[i]],0,100), discard['MouseB'], exp = i, start = 2)
plt.suptitle('Hemo Mouse B', fontsize=26)
fig = plt.figure(figsize=(10,10))
for i in range(len(dataA)):
ax = fig.add_subplot(2,2,i+1)
plotCorrHemoAlpha(ax, dataA[i], getMeanAlphaFiltered([dataB[i]],0,100), discard['MouseA'], exp = i, start = 3)
plt.suptitle('Hemo Mouse A', fontsize=26)
fig = plt.figure(figsize=(10,10))
for i in range(len(dataB)):
ax = fig.add_subplot(2,2,i+1)
plotCorrHemoAlpha(ax, dataB[i], getMeanAlphaFiltered([dataA[i]],0,100), discard['MouseB'], exp = i, start = 2)
plt.suptitle('Hemo Mouse A', fontsize=26)
fig = plt.figure(figsize=(11,10))
ax = fig.add_subplot(221)
plotCorrR(ax, dataA, getMeanAlphaFiltered(dataA,0,100), discard['MouseA'], 100, 3)
ax = fig.add_subplot(222)
plotCorrR(ax, dataA, getMeanAlphaFiltered([dataA[2]],0,100), discard['MouseA'], 100, 3)
ax = fig.add_subplot(223)
plotCorrH(ax, dataA, getMeanAlphaFiltered(dataA,0,100), discard['MouseA'], 100, 3)
ax.set_xlabel('TF all exp. mouse A')
ax = fig.add_subplot(224)
plotCorrH(ax, dataA, getMeanAlphaFiltered([dataA[2]],0,100), discard['MouseA'], 100, 3)
ax.set_xlabel('TF exp. 5 mouse A')
plt.suptitle('Mouse A Corr model vs data (lowpass fc=100Hz)', fontsize=26)
fig = plt.figure(figsize=(11,10))
ax = fig.add_subplot(221)
plotCorrR(ax, dataB, getMeanAlphaFiltered(dataB,0,100), discard['MouseB'], 100, 2)
ax = fig.add_subplot(222)
plotCorrR(ax, dataB, getMeanAlphaFiltered([dataA[2]],0,100), discard['MouseB'], 100, 2)
ax = fig.add_subplot(223)
plotCorrH(ax, dataB, getMeanAlphaFiltered(dataB,0,100), discard['MouseB'], 100, 2)
ax.set_xlabel('TF all exp. mouse B')
ax = fig.add_subplot(224)
plotCorrH(ax, dataB, getMeanAlphaFiltered([dataA[2]],0,100), discard['MouseB'], 100, 2)
ax.set_xlabel('TF exp. 5 mouse A')
plt.suptitle('Mouse B Corr model vs data (lowpass fc=100Hz)', fontsize=26)
plotRModel(dataA, mouse = 'A', exp = 2, repeat = 4,alpha = getMeanAlphaFiltered([dataA[2]],0,100) )
plotHModel(dataA, mouse = 'A', exp = 2, repeat = 4,alpha = getMeanAlphaFiltered([dataA[2]],0,100) )
plotRModel(dataA, mouse = 'A', exp = 2, repeat = 3,alpha = getMeanAlphaFiltered([dataA[2]],0,100) )
plotHModel(dataA, mouse = 'A', exp = 2, repeat = 3,alpha = getMeanAlphaFiltered([dataA[2]],0,100) )
plotRModel(dataA, mouse = 'A', exp = 1, repeat = 4,alpha = getMeanAlphaFiltered([dataA[2]],0,100) )
plotHModel(dataA, mouse = 'A', exp = 1, repeat = 4,alpha = getMeanAlphaFiltered([dataA[2]],0,100) )
plotRModel(dataB, mouse = 'B', exp = 2, repeat = 4,alpha = getMeanAlphaFiltered([dataA[2]],0,100) )
plotHModel(dataB, mouse = 'B', exp = 2, repeat = 4,alpha = getMeanAlphaFiltered([dataA[2]],0,100) )
plotRModel(dataB, mouse = 'B', exp = 0, repeat = 2,alpha = getMeanAlphaFiltered([dataA[2]],0,100) )
plotHModel(dataB, mouse = 'B', exp = 0, repeat = 2,alpha = getMeanAlphaFiltered([dataA[2]],0,100) )